Understanding the data¶
Thanks to the documentation I understood what my dataset is and that my core values are:
PRCP = Precipitation (mm or inches as per user preference)
SNOW = Snowfall (mm or inches as per user preference)
SNWD = Snow depth (mm or inches as per user preference)
TMAX = Maximum temperature (Fahrenheit or Celsius as per user preference)
TMIN = Minimum temperature (Fahrenheit or Celsius as per user preference)
Load the data¶
In [1]:
!pip install tensorflow
Requirement already satisfied: tensorflow in c:\users\pauli\appdata\local\packages\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\localcache\local-packages\python311\site-packages (2.18.0) Requirement already satisfied: tensorflow-intel==2.18.0 in c:\users\pauli\appdata\local\packages\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\localcache\local-packages\python311\site-packages (from tensorflow) (2.18.0) Requirement already satisfied: absl-py>=1.0.0 in c:\users\pauli\appdata\local\packages\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\localcache\local-packages\python311\site-packages (from tensorflow-intel==2.18.0->tensorflow) (2.1.0) Requirement already satisfied: astunparse>=1.6.0 in c:\users\pauli\appdata\local\packages\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\localcache\local-packages\python311\site-packages (from tensorflow-intel==2.18.0->tensorflow) (1.6.3) Requirement already satisfied: flatbuffers>=24.3.25 in 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[notice] A new release of pip is available: 24.3.1 -> 25.0.1 [notice] To update, run: C:\Users\pauli\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\python.exe -m pip install --upgrade pip
In [2]:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, LSTM, Dense
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
In [3]:
#Load the data
weather = pd.read_csv('weather.csv', index_col= 'DATE')
In [4]:
#Check loaded data
weather
Out[4]:
| STATION | NAME | AWND | FMTM | PGTM | PRCP | PSUN | SNOW | SNWD | TAVG | ... | WT14 | WT15 | WT16 | WT17 | WT18 | WT19 | WT21 | WT22 | WV03 | WV20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DATE | |||||||||||||||||||||
| 1963-09-05 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 0.00 | NaN | 0.0 | 0.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1963-09-06 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 0.22 | NaN | 0.0 | 0.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1963-09-07 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 0.00 | NaN | 0.0 | 0.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1963-09-08 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 0.00 | NaN | 0.0 | 0.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1963-09-09 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 0.00 | NaN | 0.0 | 0.0 | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2024-12-12 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | 16.55 | NaN | 1039.0 | 0.00 | NaN | 0.0 | 0.0 | 41.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2024-12-13 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | 8.28 | NaN | 905.0 | 0.00 | NaN | 0.0 | 0.0 | 30.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2024-12-14 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | 4.25 | NaN | 1030.0 | 0.00 | NaN | 0.0 | 0.0 | 28.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2024-12-15 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | 5.37 | NaN | 849.0 | 0.00 | NaN | 0.0 | 0.0 | 31.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2024-12-16 | USW00004781 | ISLIP LI MACARTHUR AIRPORT, NY US | NaN | NaN | NaN | 1.49 | NaN | 0.0 | 0.0 | 43.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
22377 rows × 39 columns
Prepare data¶
In [5]:
#Choose only core values from dataset
core_val_weather = weather[['PRCP', 'SNOW', 'SNWD', 'TMAX', 'TMIN']].copy()
In [6]:
#Check nwe dataset
core_val_weather
Out[6]:
| PRCP | SNOW | SNWD | TMAX | TMIN | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-09-05 | 0.00 | 0.0 | 0.0 | 64 | 53 |
| 1963-09-06 | 0.22 | 0.0 | 0.0 | 59 | 54 |
| 1963-09-07 | 0.00 | 0.0 | 0.0 | 73 | 50 |
| 1963-09-08 | 0.00 | 0.0 | 0.0 | 77 | 52 |
| 1963-09-09 | 0.00 | 0.0 | 0.0 | 75 | 52 |
| ... | ... | ... | ... | ... | ... |
| 2024-12-12 | 0.00 | 0.0 | 0.0 | 43 | 28 |
| 2024-12-13 | 0.00 | 0.0 | 0.0 | 34 | 22 |
| 2024-12-14 | 0.00 | 0.0 | 0.0 | 35 | 20 |
| 2024-12-15 | 0.00 | 0.0 | 0.0 | 42 | 20 |
| 2024-12-16 | 1.49 | 0.0 | 0.0 | 53 | 37 |
22377 rows × 5 columns
In [7]:
#Change columns names
core_val_weather.columns = ['percip', 'snow', 'snow_dep', 'temp_max', 'temp_min']
In [8]:
#Check the renamed dataset
core_val_weather
Out[8]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-09-05 | 0.00 | 0.0 | 0.0 | 64 | 53 |
| 1963-09-06 | 0.22 | 0.0 | 0.0 | 59 | 54 |
| 1963-09-07 | 0.00 | 0.0 | 0.0 | 73 | 50 |
| 1963-09-08 | 0.00 | 0.0 | 0.0 | 77 | 52 |
| 1963-09-09 | 0.00 | 0.0 | 0.0 | 75 | 52 |
| ... | ... | ... | ... | ... | ... |
| 2024-12-12 | 0.00 | 0.0 | 0.0 | 43 | 28 |
| 2024-12-13 | 0.00 | 0.0 | 0.0 | 34 | 22 |
| 2024-12-14 | 0.00 | 0.0 | 0.0 | 35 | 20 |
| 2024-12-15 | 0.00 | 0.0 | 0.0 | 42 | 20 |
| 2024-12-16 | 1.49 | 0.0 | 0.0 | 53 | 37 |
22377 rows × 5 columns
In [9]:
#Check fo NANs in new dataset
core_val_weather.isnull().sum()
Out[9]:
percip 5 snow 5 snow_dep 135 temp_max 0 temp_min 0 dtype: int64
In [10]:
#Check wheter the columns are necessary for another analysis
core_val_weather['percip'].value_counts()
Out[10]:
percip
0.00 15259
0.01 545
0.02 452
0.03 330
0.05 271
...
4.47 1
1.77 1
4.65 1
3.04 1
2.80 1
Name: count, Length: 293, dtype: int64
In [11]:
core_val_weather['snow'].value_counts()
Out[11]:
snow
0.0 21643
1.0 77
0.1 60
2.0 57
0.2 45
...
9.8 1
14.9 1
10.3 1
6.6 1
23.5 1
Name: count, Length: 95, dtype: int64
In [12]:
core_val_weather['snow_dep'].value_counts()
Out[12]:
snow_dep 0.0 20941 1.0 267 2.0 257 3.0 159 4.0 92 6.0 60 5.0 58 7.0 46 1.2 43 8.0 35 11.0 30 3.1 23 5.1 22 9.1 18 9.0 17 9.8 15 14.0 15 10.0 14 12.0 14 7.9 14 13.0 13 3.9 12 5.9 11 15.0 10 7.1 7 18.0 6 11.8 6 22.0 4 16.0 4 14.2 3 16.9 3 17.0 3 16.1 3 26.0 2 25.0 2 18.1 2 19.0 2 24.0 2 20.0 1 23.0 1 21.0 1 20.9 1 18.9 1 22.8 1 20.1 1 Name: count, dtype: int64
In [13]:
#Check columns with percipitation for analyse whe there NAN values
core_val_weather[pd.isnull(core_val_weather['percip'])]
Out[13]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1965-03-17 | NaN | 0.5 | 0.0 | 44 | 28 |
| 1965-03-18 | NaN | 0.0 | 0.0 | 40 | 34 |
| 1994-02-23 | NaN | 4.0 | 0.0 | 34 | 28 |
| 1997-02-14 | NaN | NaN | NaN | 49 | 28 |
| 1998-02-05 | NaN | 0.0 | 0.0 | 35 | 33 |
In [14]:
#see what happend before 1965-03-17 and after that
core_val_weather.loc['1965-03-10':'1965-03-25']
Out[14]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1965-03-10 | 0.00 | 0.0 | 0.0 | 45 | 34 |
| 1965-03-11 | 0.00 | 0.0 | 0.0 | 38 | 27 |
| 1965-03-12 | 0.00 | 0.0 | 0.0 | 41 | 22 |
| 1965-03-13 | 0.00 | 0.0 | 0.0 | 44 | 23 |
| 1965-03-14 | 0.00 | 0.0 | 0.0 | 45 | 21 |
| 1965-03-15 | 0.01 | 0.0 | 0.0 | 43 | 33 |
| 1965-03-16 | 0.00 | 0.0 | 0.0 | 47 | 32 |
| 1965-03-17 | NaN | 0.5 | 0.0 | 44 | 28 |
| 1965-03-18 | NaN | 0.0 | 0.0 | 40 | 34 |
| 1965-03-19 | 0.00 | 0.0 | 0.0 | 49 | 31 |
| 1965-03-20 | 0.30 | 4.0 | 0.0 | 31 | 24 |
| 1965-03-21 | 0.00 | 0.0 | NaN | 33 | 18 |
| 1965-03-22 | 0.00 | 0.0 | 0.0 | 40 | 21 |
| 1965-03-23 | 0.26 | 0.0 | 0.0 | 51 | 38 |
| 1965-03-24 | 0.21 | 0.0 | 0.0 | 42 | 33 |
| 1965-03-25 | 0.00 | 0.0 | 0.0 | 41 | 34 |
In [15]:
#Check columns with snow for analyse whe there NAN values
core_val_weather[pd.isnull(core_val_weather['snow'])]
Out[15]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-12-12 | 0.23 | NaN | NaN | 38 | 25 |
| 1997-02-14 | NaN | NaN | NaN | 49 | 28 |
| 2001-01-25 | 0.00 | NaN | 3.0 | 38 | 24 |
| 2001-10-31 | 0.00 | NaN | NaN | 58 | 36 |
| 2006-06-26 | 0.13 | NaN | NaN | 74 | 68 |
In [16]:
#see what happend before 1963-12-12 and after that
core_val_weather.loc['1963-12-01':'1963-12-17']
Out[16]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-12-01 | 0.00 | 0.0 | 0.0 | 37 | 26 |
| 1963-12-02 | 0.26 | 0.0 | 0.0 | 42 | 22 |
| 1963-12-03 | 0.13 | 0.0 | 0.0 | 40 | 33 |
| 1963-12-04 | 0.00 | 0.0 | 0.0 | 39 | 27 |
| 1963-12-05 | 0.00 | 0.0 | 0.0 | 45 | 22 |
| 1963-12-06 | 0.00 | 0.0 | 0.0 | 43 | 38 |
| 1963-12-07 | 0.00 | 0.0 | 0.0 | 47 | 30 |
| 1963-12-08 | 0.08 | 0.0 | 0.0 | 54 | 26 |
| 1963-12-09 | 0.46 | 0.0 | 0.0 | 55 | 31 |
| 1963-12-10 | 0.00 | 0.0 | 0.0 | 41 | 30 |
| 1963-12-11 | 0.00 | 0.0 | 0.0 | 36 | 27 |
| 1963-12-12 | 0.23 | NaN | NaN | 38 | 25 |
| 1963-12-13 | 0.00 | 0.0 | 0.0 | 36 | 25 |
| 1963-12-14 | 0.17 | 0.0 | 0.0 | 34 | 20 |
| 1963-12-15 | 0.00 | 0.0 | 0.0 | 27 | 16 |
| 1963-12-16 | 0.00 | 0.0 | 0.0 | 25 | 17 |
| 1963-12-17 | 0.00 | 0.0 | 0.0 | 28 | 15 |
In [17]:
#Check columns with snow depth for analyse whe there NAN values
core_val_weather[pd.isnull(core_val_weather['snow_dep'])]
Out[17]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-12-12 | 0.23 | NaN | NaN | 38 | 25 |
| 1964-01-13 | 0.76 | 8.0 | NaN | 25 | 14 |
| 1965-03-21 | 0.00 | 0.0 | NaN | 33 | 18 |
| 1969-12-18 | 0.00 | 0.0 | NaN | 36 | 17 |
| 1969-12-24 | 0.01 | 0.0 | NaN | 27 | 19 |
| ... | ... | ... | ... | ... | ... |
| 2003-12-20 | 0.00 | 0.0 | NaN | 39 | 26 |
| 2003-12-21 | 0.00 | 0.0 | NaN | 38 | 21 |
| 2006-06-26 | 0.13 | NaN | NaN | 74 | 68 |
| 2023-12-29 | 0.00 | 0.0 | NaN | 54 | 44 |
| 2024-11-04 | 0.00 | 0.0 | NaN | 57 | 34 |
135 rows × 5 columns
In [18]:
#see what happend before 1963-12-12and after that
core_val_weather.loc['1963-12-01':'1963-12-17']
Out[18]:
| percip | snow | snow_dep | temp_max | temp_min | |
|---|---|---|---|---|---|
| DATE | |||||
| 1963-12-01 | 0.00 | 0.0 | 0.0 | 37 | 26 |
| 1963-12-02 | 0.26 | 0.0 | 0.0 | 42 | 22 |
| 1963-12-03 | 0.13 | 0.0 | 0.0 | 40 | 33 |
| 1963-12-04 | 0.00 | 0.0 | 0.0 | 39 | 27 |
| 1963-12-05 | 0.00 | 0.0 | 0.0 | 45 | 22 |
| 1963-12-06 | 0.00 | 0.0 | 0.0 | 43 | 38 |
| 1963-12-07 | 0.00 | 0.0 | 0.0 | 47 | 30 |
| 1963-12-08 | 0.08 | 0.0 | 0.0 | 54 | 26 |
| 1963-12-09 | 0.46 | 0.0 | 0.0 | 55 | 31 |
| 1963-12-10 | 0.00 | 0.0 | 0.0 | 41 | 30 |
| 1963-12-11 | 0.00 | 0.0 | 0.0 | 36 | 27 |
| 1963-12-12 | 0.23 | NaN | NaN | 38 | 25 |
| 1963-12-13 | 0.00 | 0.0 | 0.0 | 36 | 25 |
| 1963-12-14 | 0.17 | 0.0 | 0.0 | 34 | 20 |
| 1963-12-15 | 0.00 | 0.0 | 0.0 | 27 | 16 |
| 1963-12-16 | 0.00 | 0.0 | 0.0 | 25 | 17 |
| 1963-12-17 | 0.00 | 0.0 | 0.0 | 28 | 15 |
Through my analysis, I observed that in each column, both before and after the missing values, there was no precipitation or only a small amount of it, no snowfall, and a snow depth of 0. Based on this pattern, I assume that on days with missing values, none of these weather phenomena occurred. Therefore, I decided filling all missing values with 0.
In [19]:
core_val_weather = core_val_weather.fillna(0)
In [20]:
#Check if there are any missing values
core_val_weather.isnull().sum()
Out[20]:
percip 0 snow 0 snow_dep 0 temp_max 0 temp_min 0 dtype: int64
In Machine Learning algorithms is important, that values I use to make predictions are numerical, so now I check if I have correct types
In [21]:
# Veryfing data types
core_val_weather.dtypes
Out[21]:
percip float64 snow float64 snow_dep float64 temp_max int64 temp_min int64 dtype: object
In [22]:
#Check index type
core_val_weather.index
Out[22]:
Index(['1963-09-05', '1963-09-06', '1963-09-07', '1963-09-08', '1963-09-09',
'1963-09-10', '1963-09-11', '1963-09-12', '1963-09-13', '1963-09-14',
...
'2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11',
'2024-12-12', '2024-12-13', '2024-12-14', '2024-12-15', '2024-12-16'],
dtype='object', name='DATE', length=22377)
In [23]:
#The index type is treated as a string, I'll convert it into datatype
core_val_weather.index = pd.to_datetime(core_val_weather.index)
In [24]:
#Check converted index
core_val_weather.index
Out[24]:
DatetimeIndex(['1963-09-05', '1963-09-06', '1963-09-07', '1963-09-08',
'1963-09-09', '1963-09-10', '1963-09-11', '1963-09-12',
'1963-09-13', '1963-09-14',
...
'2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10',
'2024-12-11', '2024-12-12', '2024-12-13', '2024-12-14',
'2024-12-15', '2024-12-16'],
dtype='datetime64[ns]', name='DATE', length=22377, freq=None)
In documentation there is a note "9’s in a field (e.g.9999) indicate missing data or data that has not been received". I'll check if there are any values like this in my dataset.¶
In [25]:
core_val_weather.apply(lambda x: (x == 9999).sum())
Out[25]:
percip 0 snow 0 snow_dep 0 temp_max 0 temp_min 0 dtype: int64
Analyzing data¶
In [26]:
#Plot temperatures from dataset
core_val_weather[['temp_max', 'temp_min']].plot()
Out[26]:
<Axes: xlabel='DATE'>
From this plot I can see, that I have complete data, that I can use in model
In [27]:
#Plot out he percip col
core_val_weather['percip'].plot()
Out[27]:
<Axes: xlabel='DATE'>
In [28]:
#Plot out he snow col
core_val_weather['snow'].plot()
Out[28]:
<Axes: xlabel='DATE'>
In [29]:
#Plot out he snow_dep col
core_val_weather['snow_dep'].plot()
Out[29]:
<Axes: xlabel='DATE'>
On each plot I see that values from these columns are fine and complete
ML model¶
In [30]:
import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(core_val_weather.corr(), annot=True, cmap='coolwarm')
plt.show()
I attempted to train my model multiple times using different combinations of values from the selected dataset. Ultimately, I decided to choose the variables with the highest correlation—maximum and minimum temperature
In [31]:
#Take values from the target column
target = 'temp_max'
values = core_val_weather[['temp_max', 'temp_min']].values
In [32]:
values
Out[32]:
array([[64, 53],
[59, 54],
[73, 50],
...,
[35, 20],
[42, 20],
[53, 37]])
In [33]:
#Normalization
data_scaler = MinMaxScaler()
values = data_scaler.fit_transform(values)
In [34]:
values
Out[34]:
array([[0.59183673, 0.70526316],
[0.54081633, 0.71578947],
[0.68367347, 0.67368421],
...,
[0.29591837, 0.35789474],
[0.36734694, 0.35789474],
[0.47959184, 0.53684211]])
In [35]:
# Use TimeSeriesGenerator to prepare the data for the LSTM model
# It automatically creates sliding windows of size `sequence_length`
# to generate input-output pairs for time series forecasting
sequence_length = 10
data_gen = TimeseriesGenerator(data=values, targets=values, length=sequence_length, batch_size=32)
In [36]:
#Split for train and test sets
train_size = int(len(data_gen) * 0.8)
data_train, data_test = train_test_split(data_gen, train_size=train_size, shuffle=False) #na True
In [37]:
data_train
Out[37]:
[(array([[[0.59183673, 0.70526316],
[0.54081633, 0.71578947],
[0.68367347, 0.67368421],
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In [38]:
data_test
Out[38]:
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[[0.40816327, 0.36842105],
[0.37755102, 0.48421053],
[0.30612245, 0.46315789],
[0.36734694, 0.46315789],
[0.48979592, 0.48421053],
[0.47959184, 0.46315789],
[0.47959184, 0.61052632],
[0.55102041, 0.58947368],
[0.37755102, 0.44210526],
[0.28571429, 0.37894737]],
[[0.37755102, 0.48421053],
[0.30612245, 0.46315789],
[0.36734694, 0.46315789],
[0.48979592, 0.48421053],
[0.47959184, 0.46315789],
[0.47959184, 0.61052632],
[0.55102041, 0.58947368],
[0.37755102, 0.44210526],
[0.28571429, 0.37894737],
[0.29591837, 0.35789474]],
[[0.30612245, 0.46315789],
[0.36734694, 0.46315789],
[0.48979592, 0.48421053],
[0.47959184, 0.46315789],
[0.47959184, 0.61052632],
[0.55102041, 0.58947368],
[0.37755102, 0.44210526],
[0.28571429, 0.37894737],
[0.29591837, 0.35789474],
[0.36734694, 0.35789474]]]),
array([[0.58163265, 0.61052632],
[0.60204082, 0.6 ],
[0.6122449 , 0.56842105],
[0.55102041, 0.61052632],
[0.54081633, 0.61052632],
[0.47959184, 0.56842105],
[0.39795918, 0.53684211],
[0.44897959, 0.56842105],
[0.47959184, 0.62105263],
[0.48979592, 0.49473684],
[0.52040816, 0.6 ],
[0.48979592, 0.49473684],
[0.51020408, 0.57894737],
[0.43877551, 0.49473684],
[0.35714286, 0.43157895],
[0.32653061, 0.38947368],
[0.34693878, 0.38947368],
[0.35714286, 0.38947368],
[0.40816327, 0.36842105],
[0.37755102, 0.48421053],
[0.30612245, 0.46315789],
[0.36734694, 0.46315789],
[0.48979592, 0.48421053],
[0.47959184, 0.46315789],
[0.47959184, 0.61052632],
[0.55102041, 0.58947368],
[0.37755102, 0.44210526],
[0.28571429, 0.37894737],
[0.29591837, 0.35789474],
[0.36734694, 0.35789474],
[0.47959184, 0.53684211]]))]
In [39]:
# Przygotowanie danych wejściowych i wyjściowych
x_train, y_train = data_train[0]
x_test, y_test = data_test[0]
In [40]:
print(f'Train X shape: {x_train.shape}')
print(f'Train Y shape: {y_train.shape}')
print(f'Test Y shape: {x_test.shape}')
print(f'Test Y shape: {y_test.shape}')
Train X shape: (32, 10, 2) Train Y shape: (32, 2) Test Y shape: (32, 10, 2) Test Y shape: (32, 2)
In [41]:
#Create LSTM model
model = Sequential([
Input(shape=(sequence_length, 2)),
LSTM(128, activation='relu', return_sequences=True, dropout=0.2),
LSTM(64, activation='relu', return_sequences=True, dropout=0.2),
LSTM(32, activation='relu', dropout=0.2),
Dense(1) #by default activation in Dense layer is linear
])
In [42]:
model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ lstm (LSTM) │ (None, 10, 128) │ 67,072 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lstm_1 (LSTM) │ (None, 10, 64) │ 49,408 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lstm_2 (LSTM) │ (None, 32) │ 12,416 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 1) │ 33 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 128,929 (503.63 KB)
Trainable params: 128,929 (503.63 KB)
Non-trainable params: 0 (0.00 B)
In [43]:
#Compile the model
model.compile(optimizer='adam', loss='mse')
In [44]:
#Add early stopping callback to monitoring val_loss. The model will stop after best weight of vall_loss
from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
In [45]:
#Train the model
train_model = model.fit(x_train, y_train, epochs=100, batch_size=64, validation_data=(x_test, y_test), callbacks=[early_stopping])
Epoch 1/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 13s 13s/step - loss: 0.4304 - val_loss: 0.4501 Epoch 2/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step - loss: 0.4126 - val_loss: 0.4279 Epoch 3/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 269ms/step - loss: 0.3896 - val_loss: 0.4062 Epoch 4/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step - loss: 0.3722 - val_loss: 0.3824 Epoch 5/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step - loss: 0.3456 - val_loss: 0.3551 Epoch 6/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step - loss: 0.3217 - val_loss: 0.3236 Epoch 7/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 258ms/step - loss: 0.3018 - val_loss: 0.2886 Epoch 8/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step - loss: 0.2713 - val_loss: 0.2505 Epoch 9/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step - loss: 0.2292 - val_loss: 0.2085 Epoch 10/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 503ms/step - loss: 0.1826 - val_loss: 0.1620 Epoch 11/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 630ms/step - loss: 0.1376 - val_loss: 0.1122 Epoch 12/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step - loss: 0.1030 - val_loss: 0.0624 Epoch 13/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step - loss: 0.0735 - val_loss: 0.0210 Epoch 14/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step - loss: 0.0577 - val_loss: 0.0067 Epoch 15/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step - loss: 0.0554 - val_loss: 0.0298 Epoch 16/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step - loss: 0.1177 - val_loss: 0.0290 Epoch 17/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step - loss: 0.1021 - val_loss: 0.0159 Epoch 18/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step - loss: 0.0920 - val_loss: 0.0066 Epoch 19/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step - loss: 0.0638 - val_loss: 0.0080 Epoch 20/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - loss: 0.0322 - val_loss: 0.0165 Epoch 21/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step - loss: 0.0454 - val_loss: 0.0269 Epoch 22/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step - loss: 0.0303 - val_loss: 0.0370 Epoch 23/100 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - loss: 0.0402 - val_loss: 0.0444
In [46]:
# Plot training & validation loss values
plt.plot(train_model.history['loss'])
plt.plot(train_model.history['val_loss'])
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.show()
In [47]:
#Predict
predicted = model.predict(x_test)
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step
In [48]:
y_test_temp_max = y_test[:, 0]
In [49]:
#Calculating errors
mae = mean_absolute_error(y_test_temp_max, predicted)
mse = mean_squared_error(y_test_temp_max, predicted)
rmse = np.sqrt(mse)
In [50]:
#Show results
print(f"Mean Absolute Error (MAE): {mae}")
print(f"Mean Squared Error (MSE): {mse}")
print(f"Root Mean Squared Error (RMSE): {rmse}")
Mean Absolute Error (MAE): 0.060304132499256924 Mean Squared Error (MSE): 0.0064897625561176845 Root Mean Squared Error (RMSE): 0.08055906253251514
In [51]:
# Rozszerzenie wymiaru predicted do (n_samples, 2)
predicted_expanded = np.zeros((predicted.shape[0], 2))
predicted_expanded[:, 0] = predicted[:, 0] # Przewidywane wartości temp_max
predicted_expanded[:, 1] = y_test[:, 1] # Rzeczywiste wartości temp_min
# Odwrotna transformacja danych
predicted_original = data_scaler.inverse_transform(predicted_expanded)
# Wyciągnięcie tylko kolumny temp_max po odwrotnej transformacji
predicted_original_temp_max = predicted_original[:, 0]
In [52]:
#Change labels to original scale
y_test_original = data_scaler.inverse_transform(y_test)
In [53]:
time_index = core_val_weather.index[-len(y_test):]
In [54]:
time_index
Out[54]:
DatetimeIndex(['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18',
'2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22',
'2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26',
'2024-11-27', '2024-11-28', '2024-11-29', '2024-11-30',
'2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04',
'2024-12-05', '2024-12-06', '2024-12-07', '2024-12-08',
'2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12',
'2024-12-13', '2024-12-14', '2024-12-15', '2024-12-16'],
dtype='datetime64[ns]', name='DATE', freq=None)
In [55]:
y_test_original[:, 0]
Out[55]:
array([76., 76., 77., 73., 73., 75., 75., 71., 69., 72., 72., 69., 68.,
71., 74., 76., 72., 62., 68., 71., 72., 75., 72., 79., 77., 56.,
57., 59., 72., 60., 60., 55.])
In [56]:
predicted_original_temp_max
Out[56]:
array([83.26999283, 82.60041559, 81.70180857, 80.56917167, 79.07015371,
77.84388733, 76.2750839 , 75.14977193, 74.25520122, 73.9877243 ,
74.35038447, 74.14900136, 73.54510903, 72.6217351 , 72.15982032,
72.14920092, 72.1841433 , 71.49786592, 71.66562676, 72.03034306,
72.00830984, 72.22076213, 72.77557039, 73.68184114, 74.82269084,
74.18903732, 73.59854484, 73.29138827, 73.65818405, 73.78392291,
73.61351013, 72.46218705])
In [57]:
# Plotting predicted and actual temperatures
plt.figure(figsize=(10, 6))
plt.plot(time_index, y_test_original[:, 0], label='Actual (temp_max)', color='blue') # Dla temp_max
plt.plot(time_index, predicted_original_temp_max, label='Predicted (temp_max)', color='red') # Dla temp_max
plt.title('Temperature Prediction vs Actual')
plt.xlabel('Time')
plt.ylabel('Temperature')
plt.legend()
plt.show()
The model performs quite well in predicting temperature over a short period following the available historical data. However, its accuracy decreases as the prediction horizon extends, which is understandable—forecasting the weather for the next day is much easier than predicting it several weeks ahead.
To improve the results, I experimented with various approaches: adjusting input data selections, refining normalization, modifying model parameters, and tweaking settings of other functions, such as sequence_length and patience. While further optimization is possible, my primary goal was to understand the process of data analysis, preprocessing, and the model’s functionality. I believe this goal has been achieved, and the project can be considered a success.
In the next stages of my career, I will focus on developing the skills I have gained and working on more advanced prediction methods.